Large machinery, such as power generation equipment, is typically very expensive to purchase, install, maintain and operate. Accordingly, determining whether such equipment is operating within desired operating parameters is important. Detecting conditions that indicate that the equipment is operating outside these desired parameters, which may result in damage to the equipment is, therefore, also important. In order to detect such conditions, sensors are typically used to measure operating parameters, such as pressure, temperature, etc., of various components and, if a predetermined threshold for a particular parameter is crossed by a particular measurement, a fault is declared. Recently, learning techniques for fault detection systems have become more prevalent in attempts to improve the accuracy of determining whether a fault exists. Well-known techniques, such as neural networks, multivariate state estimation techniques (MSET) and fuzzy logic have been used for such purposes. All such methods use historical data, collected by a plurality of sensors and indicative of past normal operations and fault conditions, to generate a model that is used to monitor future data generated by operations of the equipment. If the future data deviates too much from the historical data model, an alarm is generated and a fault is declared.
Prior fault detection methods typically relied on historical data to generate estimates of observed operational values expected to be measured by a particular sensor. Then, actual operational values were measured by the sensors and compared to the estimates. The sensor residue, or the difference between the estimate and the observed value, is then calculated and, if the residue is higher than a desired threshold, a fault is declared. However, in such prior sensor estimation techniques, estimates of a particular sensor were frequently affected by measurements taken by faulty sensors. Specifically, typical prior estimation techniques relied on measurements from several sensors measuring the same characteristic (e.g., multiple sensors measuring blade temperature in a turbine engine) to produce an estimate of the expected value from an individual sensor. Such a measurement derived from several sensors is referred to herein as a vector. These techniques typically minimized errors between the estimates and original values and, therefore, tended to spread any deviations between the values of the individual sensors among all the sensors. As a result, if one sensor was faulty and, therefore, produced a significant error in its measurement, that error would be shared by all of the non-faulty sensors, thus reducing the accuracy of the overall estimate from each of the sensors. This sharing of error is referred to herein as the spillover effect.
In order to reduce such spillover, various estimation techniques have been used, such as techniques using the well-known gradient descent functions to search for solutions. For examples of such methods, see P. J. Huber, “Robust Statistics”, Wiley-Interscience, 1981. However, these methods require the selection of a control parameter to control how quickly the function converged. Selecting such control parameters accurately is difficult. Additionally, such methods tended to converge to an optimal estimate slowly and, therefore, are impractical in many operational uses. Other attempts at reducing the effect of spillover include methods involving regression, such as the well-known kernel regression or multivariate state estimation techniques (MSET). Such techniques are described more fully in A. V. Gribok, J. W. Hines and R/E. Uhrig, “Use of Kernel Based Techniques for Sensor Validation”, Int'l Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human-Machine Interface Technologies, Washington D.C., November, 2000, which is hereby incorporated by reference herein in its entirety. However, these regression methods are computationally intensive, requiring a number of regression networks equal to the number of sensors. Additionally, such regression models are inaccurate when faulty sensors are present.